Multi-swarm　particle　swarm　optimization　algorithms　have　received　much　attention　in　recent　years　due　to　their　effectiveness　in　maintaining　the　diversity　of　population.　However,　during　the　evolution　process,　the　algorithm　often　suffers　from　the　aggregation　of　particles　within　the　sub-swarm　which　could　make　the　particles　fall　into　the　local　optimum.　In　order　to　solve　this　problem,　we　propose　a　new　multi-swarm　particle　swarm　optimization　algorithm:　A　Subordinate　Multi-Swarm　Particle　Swarm　Optimization　Algorithm　based　on　the　Dynamic　Cooperative　Learning　Strategy　Algorithm　(SMS-DCLS-PSO).　In　the　SMS-DCLS-PSO　algorithm,　we　use　the　number　of　stagnant　local　optimums　in　the　sub-swarms　as　the　aggregation　flag.　When　the　sub-swarms　reach　the　aggregation　condition,　the　dynamic　random　cooperative　learning　strategy　is　adopted　to　realize　the　adaptive　periodic　information　exchange　between　sub-swarms.　To　further　enhance　the　diversity　of　the　population,　the　best　particle　of　the　main　population　and　the　worst　particle　of　the　sub-swarms　are　respectively　mutated.　When　the　sub-swarms　don＇t　meet　the　aggregation　conditions,　the　sub-swarms　evolve　independently　which　means　the　particles　only　exchange　the　information　within　their　own　sub-swarm.　The　main-swarm　evaluates　the　local　optimal　position　of　each　sub-swarm　and　its　own　local　optimal　position　in　each　iteration　to　update　the　velocity.　The　results　of　the　comparison　with　SMS-DCLS-PSO　and　other　PSO　algorithms　on　the　latest　public　test　suite　demonstrate　that　SMS-DCLS-PSO　has　a　better　performance　on　optimization.　©　2022　ACM.